Instructions to use Zeknes/Qwen3-VL-Reranker-8B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Zeknes/Qwen3-VL-Reranker-8B-MLX-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Qwen3-VL-Reranker-8B-MLX-4bit Zeknes/Qwen3-VL-Reranker-8B-MLX-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
metadata
license: apache-2.0
library_name: mlx
pipeline_tag: text-ranking
base_model:
- Qwen/Qwen3-VL-Reranker-8B
tags:
- mlx
- multimodal rerank
- qwen
- reranker
- 4-bit
Qwen3-VL-Reranker-8B-MLX-4bit
This is the MLX 4-bit quantized version of Qwen/Qwen3-VL-Reranker-8B, optimized for Apple Silicon (Mac / iPad / iPhone) inference using the MLX framework.
Quantization Info
| Config | Value |
|---|---|
| Bits | 4 |
| Group Size | 64 |
| Quantization Mode | Affine |
| Dtype | bfloat16 |
Model Overview
- Model Type: MultiModal Reranker
- Supported Modalities: Text, images, screenshots, videos, and arbitrary multimodal combinations
- Parameters: 8B
- Context Length: 32k
- Languages: 30+
Requirements
pip install mlx-lm transformers
Usage with mlx-lm
from mlx_lm import load
model, tokenizer = load("Zeknes/Qwen3-VL-Reranker-8B-MLX-4bit")
For full usage examples (multimodal reranking, vLLM), please refer to the original model page: Qwen3-VL-Reranker-8B
Citation
@article{qwen3vlembedding,
title={Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking},
author={Li, Mingxin and Zhang, Yanzhao and Long, Dingkun and Chen Keqin and Song, Sibo and Bai, Shuai and Yang, Zhibo and Xie, Pengjun and Yang, An and Liu, Dayiheng and Zhou, Jingren and Lin, Junyang},
journal={arXiv preprint arXiv:2601.04720},
year={2026}
}